Course detail
Optimization Methods and Queuing Theory
FEKT-DKA-TK1Acad. year: 2023/2024
This study unit is made of two main parts. The first part deals with various currently used optimization methods. Students are first introduced to general Optimization theory. Then various forms of Mathematical Programming are dealt with. After the introduction into Linear and Integer Programming, the attention is given to Nonlinear Programming from its backgrounds like Convexity Theory and optimization conditions to overview and practical use of various optimization algorithms. A practically oriented introduction into Dynamic Programming with finite horizon follows. Students are also introduced into backgrounds of Stochastic Programming and Dynamic programming with infinite horizon, in particular to methods of solving Bellman's equations. The first part is closed by introduction to heuristic optimization algorithms.
The second part of the unit deals with the Queuing Theory. Various models of single queue systems and queuing networks are derived. The theory is then used by solving practical problems. Students are also introduced into simulation methods that are the only feasible solution method when a theoretical model is not available.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Offered to foreign students
Entry knowledge
Rules for evaluation and completion of the course
Aims
Developing awareness of mathematical models of Queuing Theory and their use in solving technical problems including simulation methods.
Obtaining the skills of studying, understanding, and applying mathematical models as specified in the unit contents. Ability to build mathematical programs solving particular optimization problems. Ability to use software packages that solve mathematical programs. In case of Queuing Theory it is understanding of the mathematical models and ability to apply them in practice.
Study aids
Prerequisites and corequisites
Basic literature
Sklenář, J.: Queuing Theory. Teaching notes, University of Malta, 2016. (EN)
Recommended reading
Popela, P.: Nonlinear Programming. Teaching notes, University of Malta, 2003. (EN)
Popela, P.: Stochastic Programming. Teaching notes, University of Malta, 2008. (EN)
Sklenář, J.: Dynamic Programming Theory and Applications. Teaching notes, University of Malta, 2017. (EN)
Sklenář, J.: Infinite Horizon Dynamic Programming Models. Teaching notes, University of Malta, 2017. (EN)
Sklenář, J.: Introduction to Integer Linear Programming. Teaching notes, University of Malta, 2017. (EN)
Sklenář, J.: Network Flow Models. Teaching notes, University of Malta, 2017. (EN)
Sklenář, J.: Queuing Theory - Worksheets. Teaching notes, University of Malta, 2016. (EN)
Classification of course in study plans
- Programme DKAD-EIT Doctoral 0 year of study, winter semester, compulsory-optional
- Programme DKA-TLI Doctoral 0 year of study, winter semester, compulsory-optional
- Programme DKA-TEE Doctoral 0 year of study, winter semester, compulsory-optional
- Programme DKA-SEE Doctoral 0 year of study, winter semester, compulsory-optional
- Programme DKA-MET Doctoral 0 year of study, winter semester, compulsory-optional
- Programme DKA-KAM Doctoral 0 year of study, winter semester, compulsory-optional
- Programme DKA-EKT Doctoral 0 year of study, winter semester, compulsory-optional
- Programme DKA-EIT Doctoral 0 year of study, winter semester, compulsory-optional